Machines Learn Together to Win a Competition:- Dr.Rajib Biswas given a short describes.
Summary: Training tetraplegic patients to compete in an online competition to control an avatar with BCI, researchers report the most dramatic improvements in computer augmented performance occurs when both humans and machines are able to learn.
People using brain-computer interface are more efficient when both human and machine are allowed to learn. EPFL researchers trained two tetraplegic users to compete in the international Cybathlon BCI race. Both incrementally learned how to control the BCI, and obtained the best performances at the competition, confirming researchers’ hypothesis that mutual learning plays a fundamental role in BCI training.
Brain-computer interfaces (BCIs) are seen as a potential means by which severely physically impaired individuals can regain control of their environment. BCIs use the electrical activity in the brain to control an external device. They have seen growing use in people with severe motor disabilities, for communication (by controlling a keyboard), mobility (by controlling a powered wheelchair), and daily activities (by controlling a mechanical arm or other robotic devices). But establishing such an interface is not trivial.
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